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Published on: September 17, 2019
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Interviewer effects on non-response propensity in longitudinal surveys: a multilevel modelling approach
Rebecca Vassallo1, Gabriele B Durrant1, Peter W F Smith1
1University of Southampton UK.
Summary
This study compares multilevel models to analyze interviewer effects on survey non-response. These advanced statistical methods help understand how interviewers and areas influence participation in longitudinal surveys.
Area of Science:
- Survey methodology
- Statistical modeling
- Longitudinal data analysis
Background:
- Interviewer effects can significantly impact survey data quality.
- Longitudinal surveys face challenges with participant attrition (non-response) over time.
- Traditional hierarchical models may not fully capture complex survey structures.
Purpose of the Study:
- To investigate and compare two multilevel modeling approaches: cross-classified and multiple-membership models.
- To analyze interviewer effects on wave non-response in longitudinal surveys.
- To account for non-hierarchical structures and potential confounding factors.
Main Methods:
- Application of multilevel cross-classified models.
- Application of multiple-membership models.
- Utilizing data from the UK Family and Children Survey for empirical comparison.
Main Results:
- Both multilevel approaches effectively model interviewer effects in longitudinal surveys.
- The models successfully incorporated area effects alongside interviewer influences.
- The study demonstrated the utility of these methods for complex survey designs.
Conclusions:
- Multilevel cross-classified and multiple-membership models offer robust frameworks for analyzing interviewer effects.
- These methods are valuable for understanding and mitigating non-response in longitudinal survey research.
- Accurate modeling of survey structures is crucial for reliable findings.
Keywords:
Area effectsCross-classified modelsFamily and Children StudyInterviewer effectsMultiple-membership modelsMore Related Videos
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